Sentiment Wave Dynamics and Contextual Analysis

A key innovation of Market Blade is its modeling of sentiment as a wave-like phenomenon. In an ideal scenario, a project’s sentiment evolves across all 18 dimensions in a rolling, upward trajectory:

• Day 1: Score of 1 (initial buzz).

• Day 5: Peak at 5 (growing traction).

• Day 10: Surge to 9 (peak enthusiasm), followed by a dip to 4 and subsequent waves.

This dynamic reflects organic growth, where each wave increases average sentiment and reduces the threshold for asset holder confidence. The AI tracks internal patterns within the 18 sentiments—e.g., early activation of parameters 1-4 (awareness, curiosity), followed by 11-14 (confidence, momentum)—using a recurrent neural network (RNN) layer atop the transformer.Contextual analysis enhances accuracy by weighting sentiment scores based on source credibility. Each X account is assigned an influence coefficient, calculated as:

$$\text{Coefficient} = w_1 \cdot \text{Regression Age} + w_2 \cdot \text{Citation Index} + w_3 \cdot \text{Engagement Rate}$$

$$\text{Where weights } w_1, w_2, w_3 \text{ are tuned via gradient boosting on a 1,000-account sample.} \\ \text{High-quality posts from low-influence accounts (e.g., new users) receive reduced weight,} \\ \text{while moderate content from high-influence accounts scores higher.} \\ \text{This ensures the system captures signal over noise.}$$